ISSS608 Assignment Part 2

This post is a submission for ISSS608 Assignment 1, Vast Challenge 2 part 2

Vehicle Movement by Employment Type

When we look at the pathings by Employment Type, we see a different picture. As observed below, the Employment Type “Missing”, which represents trucks (as identified with id > 100), showed a peculiar route taken. Vehicle ID 107 could be seen travelling pass Pilau Park to Velestinou Boulevard. Vehicle ID 29 from Facilities could also be seen travelling to unique places during the period.

4.2 Finding Patterns from Patronage and Vehicle Data

Next, we’ll try and identify the possible owners of the credit card and loyalty card by filtering the location by date and time. For the below charts, we filter the vehicle data to show the vehicle locations by hour, specifically on 6 Jan 2014. The method applied was to identify the vehicle id which was present in on the map on a specific hour and cross-reference it to the paronage dataset to identify the credit card transaction made as well as the location.

Isolated credit card transactions

The below observations using isolated cases could be made using the data from 6 Jan 14:

time (hr)	Vehicle ID	Location	last4ccnum
9	101	Kronos Pipe	9220
10	101	Maximum Iron	9220
10	107	Nationwide Refinery	9735
15	104	Abila Airport	8642
20	19	Shoppers	7688

The steps above was repeated for all dates to obtain the below list of identified vehicle id, tagged to a credit card number. The rest of the data was not complete as during the process, several of the credit cards were found to be used at a shop where a different vehicle was driven instead. hence the echnique was found to be lacking in terms of the accuracy.

Vehicle ID	last4ccnum
1	2681
10	1321
12	2418
13	9735
14	5368
19	7688
21	8156
24	9152 1877
32	7688
34	2681
35	9551
101	9220
104	8642
105	7792
107	9735

5.0 Future Works

As part of the future work, the network visualisation could be explored to match the credit card transactions to the vehicle id to identify the owners.